Decoding Cancer's Signals: How Immunopeptidomics is Personalizing Immunotherapy
"Unlock the Future of Personalized Cancer Treatment: Translating Immunopeptidomics into Targeted Immunotherapy for Optimal Patient Selection and Outcomes."
Immunotherapy is changing how we fight cancer, showing remarkable success, especially against tumors with many mutations. This success often relies on neo-antigens, unique markers arising from patient-specific mutations. However, to broaden the reach of immunotherapy to more cancer types and patients with fewer mutations, we need to look beyond neo-antigens to other cancer-related targets.
Mass spectrometry is a powerful tool for directly and impartially discovering and selecting tumor-specific HLA peptides, which can serve as targets for immunotherapy. Gathering these targets into a comprehensive library opens doors for multi-target therapies and speeds up clinical applications. For truly personalized treatment, it's crucial to confirm the presence of these targets on each patient's tumor to ensure both effectiveness and safety.
This article delves into how combining LC-MS with gene expression data helps define mRNA biomarkers. These biomarkers are now used in diagnostic tests like IMA_Detect™ to carefully select patients and personalize target selection within clinical trials (NCT02876510, NCT03247309). We'll explain how HLA peptide presentation translates into gene expression thresholds for companion diagnostics in immunotherapy, considering how peptides correlate to their encoding mRNA.
Unlocking Personalized Immunotherapy: How Immunopeptidomics Bridges the Gap
The increasing ability to map HLA ligandomes using mass spectrometry is paving the way for new immunotherapies. This research shows how understanding the human immunopeptidome across healthy and cancerous tissues goes beyond just finding targets; it's about validation and application. Quantifying HLA-bound peptides using label-free LC-MS is essential to confirm their correlation with mRNA measured by RNA-Seq.
- HLA Peptide Isolation and Mass Spectrometry: Integrated HLA ligandome - transcriptome analysis was performed for a set of 170 HLA-A02 positive tumor samples from 21 cancer types. Samples were surgically excised after written informed consent, snap-frozen in liquid nitrogen upon extraction and stored at -80°C until HLA precipitation. HLA peptide complexes were isolated by immunoprecipitation using the HLA-A02 specific antibody BB7.2. After ultrafiltration peptide extracts were separated by reversed-phase chromatography.
- RNA Isolation and Sequencing: In parallel to every peptide preparation, total RNA was isolated using TRIzol (Invitrogen, Karlsruhe, Germany) followed by purification with the RNeasy mini kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. RNA sequencing and expression quantification were performed by CeGaT (Tübingen, Germany). In brief, 1-2 µg total RNA were used as starting material for the library preparation performed according to the Illumina protocol.
- RNA-Seq Data Analysis: Reads were aligned against the human reference genome GRCh38.p1 and annotated with Ensembl 77 (http://www.ensembl.org). Gene expression levels were determined by extraction of read counts per exon using bedtools 2.19.1 [15] and conversion to FPKM values by normalization according to exon length and the number of mapped reads.
The Future of Cancer Treatment: Precision and Personalization
Mass spectrometry is not only enabling in-depth analysis of the human immunopeptidome, expanding available targets for immunotherapy, but also allowing for the definition of mRNA-based predictive biomarkers. These can be used as qPCR companion diagnostics in clinical studies to identify patient populations most likely to benefit from specific peptide targets, paving the way for personalized peptidomics.
The availability of these biomarkers promises to improve treatment efficacy by implementing precision medicine in cancer immunotherapies. While T cells are more sensitive in recognizing peptide/HLA complexes than current mass spectrometry, the defined thresholds offer a conservative approach. Many patients with lower expression levels could also benefit from treatment.
The suggested threshold is a starting point for early clinical studies that need to be validated and adjusted during clinical development. Combining multiple targets can create target warehouses, enabling active personalization and minimizing the chances of tumor evasion, marking a significant step towards more effective and tailored cancer treatments.